1. Field of the Invention
The present invention relates to processing of signals from antenna arrays to provide direction finding for signal sources, suppression of near field interference and noise, extension of the aperture of arbitrary antenna arrays or providing minimum redundancy array design and calibrating antenna arrays without calibration sources. More particularly, the invention includes an arrangement of actual sensors in an antenna array with a computation of higher-order statistics to provide virtual second order statistics corresponding to virtual elements in the array; employment of the actual and virtual elements of the array for covariance based direction finding; and, with the addition of a separate sensor spaced from the main array, suppression of non-Gaussian measurement noise. This is accomplished employing cross-correlation of the virtual sensors or alternatively calibrating the existing actual array employing cross-correlation of the array and its virtual sensors.
2. Prior Art
The use of discrete arrays of sensors as an antenna for receiving signals generated by multiple sources and estimating the parameters of the signals received is well known in the art. Applications of the parameter estimation include source direction finding (often identified as direction of arrival (DOA) of signal wavefronts) or, reversing the known and unknown parameters, applying known signal locations for calibration of an array of sensors of unknown array manifold.
Conventional array processing techniques utilize only second order statistics of received signals. Second-order statistics are sufficient whenever the signals can be completely characterized by knowledge of the first two moments as in the Gaussian case, however, in real applications far field sources may emit non-Gaussian signals. Exemplary of such an application is a communications scenario with multiple receivers. Failure of second-order statistics to completely characterize the signal parameters may be ameliorated by the use of higher-order statistics.
Exemplary of the prior art in array signal processing are the Ph.D. thesis by R. O. Schmidt entitled "A Signal Subspace Approach to Multi-Emitter Location and Spectral Estimation," Stanford University 1981 and U.S. Pat. No. 4,750,147 to Roy, III, et al., issued Jun. 7, 1988. The Multiple Signal Classification (MUSIC) algorithm of Schmidt and the Estimation of Signal Parameters using Rotational Invariance Techniques (ESPRIT) of Roy III employ second order statistics based on an array covariance matrix for establishing the signal parameters. The MUSIC approach requires extensive calibration of the array geometry to allow its proper characterization for use with the algorithms employed in MUSIC. The ESPRIT system alleviates the need for array calibration by employing sensor pairs having known spacing and orientation while allowing variable geometry with respect to the pairs. The ESPRIT system consequently requires doubling the number of sensors with commensurate added cost and merely replaces the problem of calibration of the overall array manifold with the requirement for strict orientation of the sensor pairs in the array.
Both the MUSIC and ESPRIT systems are designated as subspace parameter estimation algorithms. Various methods have been recommended for increasing the capability of such subspace algorithms by the use of higher-order statistics for noise reduction. See, e.g., Shamsunder, S. and Giannakis, G., "Modeling of Non-Gaussian Array Data Using Cumulants", IEEE Transactions on Signal Processing, March 1993; Pan, R. and Nikias, C. L., "Harmonic Decomposition Methods in Cumulant Domains", Proceedings ICASSP '88, pp. 2356-2359 New York, N.Y., April 1988; and Chiang, H. H. and Nikias, C. L., "The ESPRIT Algorithm With Higher-Order Statistics," Proceedings Vail Workshop, Higher-Order Spectral Analysis, pp. 163-168, June 1989. Such applications of higher-order statistics provide techniques for noise reduction/elimination, however, the approches as disclosed in the prior art are supplementary to the conventional array processing techniques.
The present invention overcomes the difficulties of the subspace algorithms and provides an integrated approach to the use of higher-order statistics for direct calculation of estimated parameters as opposed to mere signal correction.